Cross-platform- and subgroup-differences in the well-being effects of Twitter, Instagram, and Facebook in the United States

Spatial aggregates of survey and web search data make it possible to identify the heterogeneous well-being effects of social media platforms. This study reports evidence from different sources of longitudinal data that suggests that the well-being effects of social media differ across platforms and population groups. The well-being effects of frequent social media visits are consistently positive for Facebook but negative for Instagram. Group-level analyses suggest that the positive well-being effects are experienced mainly by white, high-income populations at both the individual and the county level, while the adverse effects of Instagram use are observed on younger and Black populations. The findings are corroborated when geocoded web search data from Google is used and when self-reports from surveys are used in place of region-level aggregates. Greater Instagram use in regions is also linked to higher depression diagnoses across most sociodemographic groups.

In the recoded variable for the analysis, responses 3 and 4 were discarded, The final measure reflected the fraction of respondents per county per year who reported that they had been diagnosed with depression.
County-level demographic information: Four demographic statistics (median age, % population over 65 years, % African American population and % Hispanic population, % While population), two socioeconomic status (SES) indicators (% population with a Bachelor's degree, logged average per capita income for individuals over the age of 25 in $) compiled by the US County Health Rankings and Roadmaps 1 were used as socio-demographic covariates.

Simmons National Consumer Survey data Individual social networking sites:
From 2015 onward, individual questions about the use of Facebook, Twitter, and Instagram were used to collect weekly visits and worded as follows: "Which of these websites have you visited in the last seven days?" • Facebook.com • Twitter.com • Instagram.com Monthly visit information was collected with the following question: "How many times did you visit each of these websites in the last 30 days?" • 1-5 • 6-15 • 16 or more A weighted average was used to calculate the average weekly and monthly visits of the entire population. These two variables were strongly correlated with each other. An arithmetic mean rescaled from 0 to 1 served as the final measure, where a higher value reflected more visits to an individual social media site.

Validation with Pew Research data
Pew Tracking Surveys are conducted twice a year on a stratified nationally representative sample. In each of eleven surveys between 2008 -2013, respondents were asked about their satisfaction with life and their social media use. Additionally, information was collected about the quality of their internet access. Finally, respondents reported their age, gender, race, income, and education. Not all the surveys collected the state of residence, so this information could not be included in the analysis.

Life Satisfaction
Overall, how would you rate the quality of life for you and your family today? Would you say it is... excellent, very good, good, fair or poor? Responses were reverse-coded before analysis: • According to the documentation provided by Pew Research, the item wording for this question from December 2012 until May 2013 was -"At home, do you connect to the internet through a dial-up telephone line, or do you have some other type of connection, such as a DSL-enabled phone line, a cable TV modem, a wireless connection, or a fiber optic connection such as FIOS?" December 2012 and earlier trend question wording included "T-1" as a read category. From September 2009 thru January 2010, the question asking about type of home internet connection (MODEM) was form split. MODEMA was asked of Form A respondents who use the internet from home. MODEMB was asked of Form B respondents who use the internet from home.
In the recoded variable used for analysis, the response reflects whether a respondent had (Download speed = 1) or did not have (Download speed = 0) at least a DSL connection at their home.

Social media use
According to the documentation provided by Pew Research, the item wording for this question from August 2011 until May 2013 was -"Do you ever use the internet to use a social networking site like Facebook, LinkedIn or Google Plus?" From April 2009 through August 2011, the item wording for this question was "Do you ever use a social networking site like MySpace, Facebook or LinkedIn?" In December 2008, item wording was "Use a social networking site like MySpace or Facebook." In August 2006, item wording was "Do you ever use an online social networking site like MySpace, Facebook or Friendster?" Prior to August 2006, item wording was "Do you ever use online social or professional networking sites like Friendster or LinkedIn?" In the recoded variable used for analysis, the response reflects whether a respondent used (Social media use = 1) or did not use (Social media use = 0) social media.

Results
The well-being effects of general social media use Table 1(a) reports the association between general social media use (visit frequency) and well-being at the county level for 20013-2018 in the primary dataset. The first column reports the results of a model that included only the dataset-and year-fixed effects. Next, column 2 included respondent fixed effects. According to Column 2, a 1% increase in social media use predicts an 0.6% rise in well-being (β = 0.64, p <0.01).
The findings are validated against individual-level associations between social media use and life satisfaction between 2008-2013 in Table 1(b). The secondary data from Pew was not available after 2013 because Pew did not collect these survey items after 2013, and does not offer granular information about the social media platform being visited. The first column reports the results of a model that included only the dataset-and year-fixed effects. Next, column 2 included state fixed-effects. Finally, Column 3 included respondent fixed-effects.
Across models, the increase in social media use is associated with higher well-being. Furthermore, in all specifications, the relationship between the download speed and well-being was positive, although not significant after including respondent fixed effects, suggesting that physical barriers to social media access may also play a role when people self-select into using social media. According to Column 3, after controlling for sociodemographic and exogenous variations, a 1% increase in social media use predicts a 0.08% rise in well-being (β = 0.08, p <0.001). Figure 1(a) reports the association of general social media visits and well-being, as regressed against the county-level measures of satisfaction from 1.73 million responses to the Gallup-Sharecare Well-Being Index from 2014-2018 after including cross-lagged and fixed effects. The effects are pooled at the year-level. Figure 1(b) reports that the county-level findings bear out individual-level findings with Pew Track Surveys data from 2008-2013, where the increase in social media use is associated with higher well-being. By and large, the effects show the same positive trend over time.

Platform-specific well-being effects
The detailed results underlying Figure 2 are reported in Table 2. They are explained in the main text.

Platform-specific, group-level well-being effects
The detailed results underlying Figure 3 are reported in Table 3 which allows region-level inferences. Some of the many interesting differences and reported in the following paragraphs.
First, consider the effects of Facebook use on well-being across different demographic groups. The trends are steady, and across all groups the increase in Facebook use is typically associated with a statistically significant increase in well-being (0.04 <= β <= 0.19). Next, consider the group-level differences in the well-being effects of Twitter use. There is more variance here, but the statistically significant effects on well-being are negative and seen in counties with poor internet access (counties with broadband not available) (β = -0.08, p < 0.05), counties with a large population over 65 years (counties in the top quantile of a senior citizen population) (β = -0.17, p < 0.01), and counties with a large White population (β = -0.22, p < 0.001).
Finally, consider the group-level differences in the well-being effects of Instagram use. Counties with a large population under 18 years (counties in the top quantile of a population under the age of 18 years) report a 0.14% decrease in well-being with a 1% increase in Instagram use (β = -0.14, p < 0.01). On the Figure 1. Estimates of the association between social media visit frequency and well-being with (a) the primary dataset at the county-level, and the (b) the secondary dataset at the individual-level. Estimates are pooled by year for (a) and by sample for (b). In all but one year and one sample, general social media visit frequency had a positive association with well-being (4.0<= β <=2.0 at the county-level; -0.05< β <0.2 at the individual-level)  Social media use 0.11 * * * 0.13 * * * 0.08 * * *  other hand, counties with a large White population (counties in the top quantile of the population of Whites, as per the National Census figures) report a 0.06% increase in well-being with a 1% increase in Instagram use (β = 0.06, p < 0.01). In contrast, counties in the top quantile of Black populations report a decrease (0.14%) in well-being for every 1% increase in Instagram use at the county level (β = -0.14, p < 0.01). A validation analysis was performed on the tertiary dataset. Group-level differences reported in Table 4 suggest that even at the individual level, greater social media use is predictive of higher well-being for high-income respondents (β = 0.09, p < 0.01), White respondents β = 0.12, p < 0.001, and those with good internet access (DSL connections in their homes) (β = 0.08, p < 0.001). On the other hand, higher social media use is predictive of lower well-being for Black respondents at the individual level (β = -0.13, p < 0.05). Table 4. Secondary dataset: Subgroup differences in the association of social media use with well-being (2008( -2013 Research data at individual level). All models include all other covariates (age, gender, race, income, internet access quality, education) except the one that defines the subgroup. Generalizability to mental health outcomes Table 5 reports group-level differences in the association of social media use with self-reported depression diagnosis from the Gallup-Sharecare data, aggregated and averaged to the county-level. The associations at the high level lie in the opposite directions. However, drilling into demographic groups fulfills most expectations, where in all groups except counties with a larger Hispanic population, greater Facebook use is predictive of lower depression diagnoses (-0.22 <= β <= -0.10, p< 0.05). In all but the counties with broadband, greater Twitter is consistently predictive of higher depression diagnoses (0.11 <= β <= 0.37, p < 0.01). Instagram use predicts higher depression diagnoses in both, counties with good internet access and high income (0.11 <= β <= 0.12, p< 0.05) but also counties with no broadband access (β = 0.07, p < 0.05). Similar effects are seen for counties with either a large young or a large 6/7 senior citizen population (0.14 <= β <= 0.21, p< 0.05). There appear to be no differential effects of Instagram use in counties with more or fewer ethic populations. Table 5. Primary dataset: Subgroup differences in the association of social media use with depression diagnoses (2016-2018, NCS data and Gallup-Sharecare data at county-level). All models are cross-lagged and include all other covariates (age, gender, race, income, internet access quality, education) except the one that defines the subgroup. Note: + p<0.1; * p<0.05; * * p<0.01 ; * * * p<<0.001